Triple
T9910868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philipp Lahm |
E185138
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lahm |
E185138
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Lahm | Statement: [Philipp Lahm, familyName, Lahm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lahm Context triple: [Philipp Lahm, familyName, Lahm]
-
A.
Philipp Lahm
chosen
Philipp Lahm is a retired German footballer widely regarded as one of the best full-backs and defensive midfielders of his generation, known for captaining Germany to the 2014 World Cup title.
-
B.
Dirk Hatteraick
Dirk Hatteraick is a ruthless Dutch smuggler and villain in Sir Walter Scott’s novel "Guy Mannering."
-
C.
Klose
Klose is a German former professional footballer best known as the all-time top scorer in FIFA World Cup history.
-
D.
Andreas Brehme
Andreas Brehme is a former German footballer best known for scoring the winning penalty in the 1990 FIFA World Cup final and for his successful club career in Germany and Italy.
-
E.
Jürgen
Jürgen is a masculine given name of German origin, commonly used in German-speaking countries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca8296165881908ca4750701af1f29 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb512a26881908eb72a21ffb1efef |
completed | April 2, 2026, 12:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d228a405908190ad63ebd659779ed7 |
completed | April 5, 2026, 9:17 a.m. |
Created at: March 30, 2026, 8:41 p.m.